install.packages("readxl")
library(readxl)

Pytania badawcze:

  1. jakie najtańsze a jakie najdroższe
  2. jaka dzielnica jest najdroższa
  3. gdzie jest najwięcej nieruchomości (dzielnice)
  4. rok vs cena
  5. ceny developera (ataner) -
  6. mieszkania dla studentów (dostępność, dzielnice)
  7. osoby niepalące -
  8. odsetek mieszkań umeblowanych
  9. średnia wielkość pokoju
  10. cena vs metraż

Wczytujemy dane z pliku csv

rent <- read.csv2(file = "../data-raw/rent-poznan.csv")
head(rent)

Wczytamy dane z Excela

rent <- read_excel(path = "../data-raw/rent-poznan.xlsx")
rent <- as.data.frame(rent)
head(rent)

Wybieramy kolumny ze zbioru rent

rent[, 1] # w data.frame wybór jednej kolumny skutkuje wektorem
   [1] "19576742" "20292499" "25876297" "30574847" "30774505" "31073093" "31142157" "31238621" "31787215"
  [10] "32732377" "32735125" "32737325" "32739025" "33197305" "33603847" "34142495" "34353551" "34387075"
  [19] "34603491" "34674153" "35498262" "35614352" "35697526" "35803750" "35809836" "35838188" "35859230"
  [28] "35877716" "36022372" "36108930" "36140878" "36306458" "36394558" "36506902" "36508920" "36634154"
  [37] "36679966" "36684056" "36706458" "36781616" "36938970" "36960066" "37054002" "37413562" "37413604"
  [46] "37447450" "37467842" "37759632" "37769632" "37874316" "37978832" "38032336" "38207096" "38473604"
  [55] "38535178" "38643674" "38846288" "38887054" "38992724" "39018820" "39150724" "39238722" "39258710"
  [64] "39415476" "39487112" "39525908" "39585954" "39659156" "39671614" "39709748" "39749960" "39819436"
  [73] "39836910" "39874374" "39876904" "39982726" "40110596" "40157932" "40307840" "40345152" "40349761"
  [82] "40407741" "40514743" "40521895" "40588427" "40651069" "40686301" "40689573" "40706785" "40718147"
  [91] "40760747" "40765773" "40782409" "40829297" "40833711" "40846685" "40851365" "40858679" "40862067"
 [100] "40867671" "40876079" "40916831" "40929957" "40999579" "40999581" "41065535" "41080707" "41107665"
 [109] "41135499" "41154065" "41189175" "41195733" "41200665" "41259217" "41314501" "41384625" "41599391"
 [118] "41608879" "41690557" "41713611" "41755263" "41800921" "41950159" "41960421" "41960693" "41983539"
 [127] "42007649" "42090221" "42150409" "42158463" "42214479" "42222033" "42226313" "42231861" "42235235"
 [136] "42243651" "42268761" "42298337" "42314295" "42339715" "42384859" "42485851" "42595097" "42627047"
 [145] "42658889" "42698649" "42703639" "42729723" "42739025" "42800313" "42800751" "42805623" "42812985"
 [154] "42850703" "42856493" "42915723" "42981347" "42982971" "43024953" "43027549" "43027993" "43087891"
 [163] "43113203" "43116185" "43122383" "43165963" "43172691" "43188483" "43203785" "43214193" "43220477"
 [172] "43234277" "43305573" "43316837" "43316839" "43332723" "43333663" "43340707" "43348479" "43394919"
 [181] "43395411" "43438467" "43481943" "43547165" "43548273" "43573143" "43601551" "43605709" "43642531"
 [190] "43661397" "43676621" "43686853" "43693897" "43763469" "43763507" "43766355" "43777507" "43787485"
 [199] "43815105" "43828159" "43831911" "43897690" "43897966" "43929608" "43936564" "43940350" "43990250"
 [208] "43991830" "43997650" "44006908" "44014108" "44025192" "44049926" "44060522" "44083368" "44083786"
 [217] "44087540" "44103206" "44126668" "44151650" "44171988" "44172292" "44251100" "44293700" "44304322"
 [226] "44351882" "44385390" "44393654" "44398400" "44419392" "44434124" "44435450" "44443794" "44592932"
 [235] "44655882" "44681384" "44694664" "44694982" "44700204" "44704618" "44715976" "44741178" "44750014"
 [244] "44778422" "44787744" "44802040" "44836994" "44843034" "44848056" "44872030" "44872956" "44880112"
 [253] "44880190" "44897338" "44916652" "44988856" "44999100" "44999486" "45027940" "45029258" "45045524"
 [262] "45052160" "45089994" "45113662" "45115052" "45125050" "45129264" "45129604" "45153524" "45162402"
 [271] "45169752" "45179856" "45188848" "45198124" "45241186" "45258448" "45269034" "45273822" "45288018"
 [280] "45294340" "45295738" "45312868" "45324284" "45335880" "45336054" "45337888" "45351114" "45396816"
 [289] "45411050" "45421094" "45440942" "45441086" "45485790" "45493294" "45499048" "45504854" "45506746"
 [298] "45536106" "45589606" "45613076" "45620290" "45641308" "45708170" "45719928" "45721026" "45721042"
 [307] "45733068" "45751084" "45757586" "45770882" "45776810" "45785730" "45889972" "45893808" "45914984"
 [316] "46075546" "46102906" "46105380" "46123846" "46130316" "46137814" "46142106" "46142250" "46180134"
 [325] "46228438" "46231138" "46242236" "46271182" "46279330" "46339548" "46362304" "46386354" "46399510"
 [334] "46407982" "46411700" "46483384" "46493380" "46494664" "46535186" "46558528" "46622082" "46666600"
 [343] "46682278" "46720162" "46727282" "46739384" "46754618" "46755266" "46813994" "46817474" "46833142"
 [352] "46875122" "46877054" "46880614" "46883766" "46910770" "46928378" "46931846" "47031250" "47032176"
 [361] "47035324" "47048872" "47070620" "47070802" "47073060" "47083158" "47133406" "47139598" "47158528"
 [370] "47187834" "47200596" "47203828" "47210238" "47217488" "47218380" "47220328" "47224278" "47237670"
 [379] "47240280" "47257522" "47266048" "47266550" "47274980" "47296062" "47335030" "47352248" "47364260"
 [388] "47364992" "47367776" "47386890" "47396280" "47404764" "47439708" "47474448" "47489864" "47508898"
 [397] "47518338" "47530080" "47539050" "47564930" "47577996" "47651096" "47665428" "47680896" "47686508"
 [406] "47714048" "47758798" "47763142" "47774320" "47782626" "47791546" "47810292" "47826532" "47860328"
 [415] "47876950" "47901976" "47906880" "47975342" "47989888" "48004288" "48093240" "48102926" "48120778"
 [424] "48129666" "48138024" "48141012" "48141092" "48143212" "48147962" "48188760" "48195860" "48198326"
 [433] "48214012" "48218528" "48223278" "48225508" "48225540" "48243796" "48271026" "48305096" "48339770"
 [442] "48343388" "48344886" "48354498" "48359386" "48373920" "48405764" "48406654" "48433752" "48460846"
 [451] "48466668" "48467564" "48485520" "48495012" "48528176" "48532654" "48545388" "48557344" "48565074"
 [460] "48574706" "48575592" "48606970" "48611346" "48624316" "48634096" "48634442" "48648010" "48652250"
 [469] "48658044" "48667934" "48682484" "48689616" "48690626" "48704622" "48707684" "48717872" "48718226"
 [478] "48735494" "48743792" "48751364" "48770192" "48786324" "48802508" "48824982" "48826946" "48827676"
 [487] "48828088" "48828234" "48854202" "48861346" "48886382" "48889162" "48960630" "48960884" "48969696"
 [496] "48969774" "49010106" "49030336" "49030378" "49037600" "49039542" "49048892" "49054224" "49080258"
 [505] "49080826" "49083578" "49090746" "49099006" "49106772" "49109718" "49125652" "49134600" "49138096"
 [514] "49149038" "49155600" "49163732" "49165436" "49170722" "49172970" "49180720" "49184378" "49184742"
 [523] "49201282" "49212976" "49222778" "49232916" "49240994" "49248720" "49251494" "49252638" "49254978"
 [532] "49260744" "49261278" "49261432" "49261568" "49262246" "49265394" "49286224" "49302098" "49307066"
 [541] "49316868" "49318660" "49323442" "49347988" "49354350" "49356244" "49358672" "49362350" "49364332"
 [550] "49371618" "49372000" "49377894" "49383836" "49390614" "49399864" "49402118" "49429070" "49447114"
 [559] "49447372" "49447824" "49449006" "49449776" "49450634" "49462794" "49466620" "49482489" "49488915"
 [568] "49502562" "49502751" "49504395" "49538025" "49547133" "49548504" "49549122" "49549245" "49558167"
 [577] "49559019" "49562073" "49566900" "49567890" "49593603" "49609487" "49612283" "49623447" "49630395"
 [586] "49682007" "49683235" "49693067" "49703271" "49735859" "49787507" "49804788" "49826304" "49854316"
 [595] "49871428" "49882084" "49892244" "49906312" "50006500" "50010188" "50012148" "50017220" "50102336"
 [604] "50145460" "50149908" "50197688" "50235080" "50270772" "50324076" "50332084" "50395592" "50419520"
 [613] "50468964" "50472696" "50501368" "50503456" "50519528" "50523384" "50527796" "50629412" "50632356"
 [622] "50651016" "50672712" "50741516" "50750348" "50759432" "50776368" "50784140" "50789312" "50805760"
 [631] "50864580" "50903252" "51052556" "51069788" "51093676" "51097200" "51121404" "51126324" "51165576"
 [640] "51262336" "51266864" "51399072" "51409968" "51416408" "51443272" "51443648" "51490916" "51550260"
 [649] "51551876" "51553048" "51556236" "51556420" "51563200" "51576692" "51577636" "51619244" "51671364"
 [658] "51675648" "51716744" "51717600" "51732300" "51733892" "51762380" "51770952" "51791092" "51810520"
 [667] "51820884" "51823628" "51898724" "51900188" "51938848" "51939640" "51958040" "51966736" "51971032"
 [676] "51979696" "51990976" "52070539" "52085811" "52090751" "52092335" "52096287" "52109147" "52112551"
 [685] "52180595" "52205635" "52205899" "52206759" "52207075" "52235327" "52286179" "52296223" "52303247"
 [694] "52326875" "52327491" "52327531" "52356191" "52365151" "52368135" "52450331" "52455083" "52457179"
 [703] "52461235" "52464187" "52464699" "52464955" "52478483" "52482511" "52490611" "52494415" "52534163"
 [712] "52538155" "52538959" "52564483" "52578151" "52635379" "52637047" "52651043" "52661799" "52674243"
 [721] "52694767" "52695523" "52710115" "52722719" "52734119" "52741619" "52749403" "52769127" "52772103"
 [730] "52774091" "52777755" "52781227" "52781367" "52785295" "52796883" "52814743" "52849863" "52874191"
 [739] "52875835" "52877551" "52880915" "52908727" "52918143" "52944071" "52966283" "52968011" "52968975"
 [748] "52971043" "52996771" "53022971" "53031095" "53048015" "53061039" "53084835" "53091027" "53093971"
 [757] "53123683" "53128255" "53138643" "53157543" "53180700" "53185928" "53202504" "53213920" "53215180"
 [766] "53252704" "53266440" "53283072" "53291116" "53291632" "53305308" "53336684" "53337116" "53338856"
 [775] "53363316" "53372440" "53393812" "53396360" "53418156" "53426344" "53437820" "53438592" "53442568"
 [784] "53458828" "53478744" "53481412" "53493260" "53508304" "53514344" "53516060" "53521636" "53539488"
 [793] "53558164" "53560576" "53574808" "53575288" "53575312" "53584916" "53590244" "53630656" "53631048"
 [802] "53638480" "53646644" "53647056" "53647572" "53659388" "53660964" "53670064" "53673664" "53688636"
 [811] "53697416" "53703620" "53723848" "53728888" "53731220" "53731560" "53737484" "53740040" "53758400"
 [820] "53780328" "53780468" "53786604" "53797796" "53808896" "53809160" "53814860" "53855840" "53858052"
 [829] "53880320" "53895620" "53908000" "53909328" "53922208" "53940944" "53943212" "53978028" "53986068"
 [838] "54002428" "54009940" "54014872" "54015744" "54017756" "54039080" "54041908" "54062548" "54072664"
 [847] "54083476" "54106788" "54108064" "54109904" "54120364" "54150216" "54155800" "54157356" "54165336"
 [856] "54171540" "54171640" "54177956" "54180384" "54182668" "54195744" "54235660" "54236312" "54255996"
 [865] "54274584" "54275632" "54281512" "54299288" "54320140" "54322780" "54329904" "54331776" "54346460"
 [874] "54373864" "54374648" "54376316" "54377288" "54377896" "54378004" "54381012" "54381240" "54381324"
 [883] "54394052" "54400708" "54429680" "54430100" "54447744" "54449328" "54462972" "54469796" "54470868"
 [892] "54487764" "54490860" "54491976" "54509104" "54511800" "54514144" "54528924" "54536760" "54563032"
 [901] "54579352" "54585380" "54586468" "54603824" "54604100" "54612744" "54616412" "54620656" "54621772"
 [910] "54628572" "54629880" "54636564" "54645420" "54675648" "54682268" "54682772" "54689796" "54702460"
 [919] "54704528" "54712940" "54724936" "54736856" "54745148" "54749916" "54753208" "54754368" "54760156"
 [928] "54763236" "54769924" "54781596" "54782672" "54789096" "54800512" "54814808" "54821436" "54836036"
 [937] "54838764" "54841916" "54842180" "54852440" "54866264" "54866944" "54875012" "54875584" "54881960"
 [946] "54889540" "54900356" "54901376" "54917772" "54918848" "54924404" "54924672" "54938324" "54942804"
 [955] "54980380" "54985312" "54987340" "54995580" "55000056" "55002292" "55036640" "55037324" "55039208"
 [964] "55052168" "55055984" "55057276" "55063540" "55063660" "55075132" "55078748" "55080684" "55081040"
 [973] "55082500" "55091420" "55114576" "55115372" "55121108" "55130888" "55137192" "55143740" "55149876"
 [982] "55150356" "55151004" "55153896" "55156080" "55165096" "55168456" "55192880" "55210024" "55216480"
 [991] "55216936" "55219464" "55224420" "55224848" "55227228" "55227428" "55227680" "55227744" "55235764"
[1000] "55239384"
 [ reached getOption("max.print") -- omitted 15448 entries ]
rent[, 1, drop = FALSE] # drop = FALSE mówi, że mamy dostać ramkę danych
rent[, 1:10]
rent[, ncol(rent), drop = FALSE] # ostatnia kolumna
rent[, c(1, 5, 10)]
rent[, c("id", "price", "flat_area")]
rent$price ## wektor
   [1]  1400  4600  2300  1600  1200  1800  1500  1400  1300  1200  2000  2000  1600  1400  1990  1290
  [17]  1200  1800  1300  1500  1800  1500  3400  1400  1149  1400  1100  1000  1600  1600  1650  1200
  [33]  1300  1300  3200  1980  1550  1600  1200  1100  1300  1400  1700  1850  1900  1900  2100  1300
  [49]  1250  2400  1650  1500  2300  1670  1300  1200  1650  1100  1100  1700  1300  1450  2400  1100
  [65]  1350  1600  2099  1300  2200  1300  2000  1300  1500  1550  2500  1600  1800  1300  1450   970
  [81]  1700  1700  1600  2500  1600  2350  1500  1650  3500  2300  3000  3000  1500  1590  2250  1600
  [97]  1600  2000  1250  1600  1400  1350  1500  1490  2500  2800  2200  1400  2600  1390  1900  1000
 [113]  1200  2400  1700  3500  2500  2950  1100  1800  1500  1400  1500  1400  1450  1200  1400  1490
 [129]  1690  1500  1300  2000  1300  1800  1300  1999  2200  1950  2400  1600  1800  1800  1350  2800
 [145]  2200  1000  1100  1700  2200  1390  1450  1199  1400  1650  1300  1200  1300  1150  1800  2500
 [161]  1600  2000  1800  2000  1900  1320  1950  1650  1100  1750  1200  1200  1800  1300  1500  2200
 [177]  1400  1100  1150  1450  2300  1350  1600  1250  1050  1200  1000  1390  1350  1350  2500  1700
 [193]  1600  1430  1950  1700  1300  2200  1150  2590  1500   950  1700  1350  1130  1250  1400  1500
 [209]  1600  1750  1450  1600  1500  1290  1250  1800  1790  1200  1600  1650  2000  2800  1700  1500
 [225]  1450  3550  1800  1500  1900  1700  1100  1450  1900  1000  1300  1350  2300  1400  1600  1800
 [241]  1600  1600  1400   550  1600  1500  1499  3200  1200  1750  1750  1300  2990   700  1700  1500
 [257]  1350  2650  1600  1990  1099  1300  1700  1600  1800  1330  1890  1700  1449  1200  1650  1550
 [273]  1500  1900  2200  1700  2200   290  1550  2000   800  2300  1350  1500  1500  1000  1350  1700
 [289]  1750  1520  1500  1400  1750  1500  1400  1800  2300  2200  1000  1000  1400  1600  2000  1799
 [305]  1150   950  3300  3000  2400  1000  2800  1800  2500  1550  1700  1299  2300  1800  1600  1750
 [321]  1350  2300  1600  1300  1200  1250  2400  1300  1950  2300  1600  1950  1500  1500  1600  1650
 [337]  1000  1600  1850  1600  2100  1650  2000  2000   970  1750  2600  1300  1600  1200  1300  7500
 [353]  1400  1250  1400  1750  1050  2900  1700  1380  1800  1350  2300  1350  1250  1350  1400  1250
 [369]  1800  2000  1900  1850  2800  1250  1950  1600  1300  1390  1650  1800  1300  1700  1600  1500
 [385]  2000  1450  1300  1450  1450  1550  1800  1600  1300  1100  1500   900  1600  1300  1200  1700
 [401]  1200  1700  1480  1800  1500  1990  1950  1100  1800  1200   900  1200  1390  1600  1200  3900
 [417]  1650  3550  1200  1250  1700  3800  1580  2000  1600  1550  1100  1500  1950  1700  1450  1600
 [433]  2100  1950  2200  1600  1700  1500  1200  1300  1700  1200  1800  1950  1600  2250  1699  1900
 [449]  1500  2250  1700  1300  2000  1600  3500  2100  1100  2300  1500  1450  2300  1500  9900  2200
 [465]  1670  2400  1199  1900  1390  1200  1500  1800  1700  1700  1300  1500  2000  1400  1250  1550
 [481]  2990  2500  1500  1400  2200  2000  1800  1500  2000  2000  2800  1800  1300  2100  2100  1350
 [497]  1700  1600  2000  1580  2100  3500  2500  1300  1200  1600  1200  1200  1600  2500  1300  1290
 [513]  1600  2000  2100   980  1350  1200  4200  1600  1500  2200  2150  1600  2300  1100  3000  1600
 [529]  1600  1400  2600  1700  1400  1600  1900  2100  1600  1300  1700  1400  1800  1100  1900  1650
 [545]  2200  1350  1600  1400  1300  1500  1100  1700  2300  1600  1500  1900  2400  1800  2100  1200
 [561]  1250  2000  1600  1700  2100  1199  1100  1250  1200  1400  2000  1650  1900  2200  2800  2100
 [577]  1500  1690  1650  2700  1500  1500  1540  1850  1500   750  1250  1300  1400  1300  1650  2200
 [593]  3900  1200  1650  1500  1400  1400  1400  1950  1500  1450  2000  1000  1600  1250  2100  2450
 [609]  1400  1650  2600  1700  1850  1950  1900  2250  1200  1800  1200 13900  1700  2100  1300  1300
 [625]  1700   900  2000  1300  1400  2300  1800  1900  2500  2300  2700  2250  1450  1750  1200  1300
 [641]  1500  1800  1590  1400  2000  2400  1850  2400  1800  2000  1750  2750  6500  1500  1200  1700
 [657]  1450  2300  1200  2200  1650  2150  1600  1420  2200  1950  1100  1800  1200  1600  3200  3300
 [673]  1150  3500  2500  1600  1200  1750  1700  1600  2500  2200  1200  1599  2500  1390  1700  1400
 [689]  1600  2100   850  2200  2400  1000  1800  1850  1700  2300  1650  1650  1400  1900  2150  1350
 [705]  1650  1700  1500  1650  2100  1290  2300  1400  1390  1300  1300  1100  1400  1450  2000  1500
 [721]  1000  1800  1300  1590  2000  2500  2000  1000  1150  1850  1500  2800  3000   900  2200  1500
 [737]  1400  1000  1800  1800   990  1650  1500  1300  2400  1800  2500  1900  1600  3000  1600  1200
 [753]  2500  1200  2600  5000  1500  1100  1100  3900  1250  1950  1600  1650  1150  1600  1600  2300
 [769]  1700  2500  1200  1100  2500  1800  1700  1300  1250  1450  2300  1250  1100  1400  1550  1500
 [785]  1200  1500  1900  1700  1650  1700  2700  1800  1150  1520  2090  1700  2350  1800  2200  1600
 [801]  1850  1400  2300  1650  2200  1350  1500  3800  1550  1900  1800  2700  2100  1300  1450  1700
 [817]  2000  2000  2100  2600  1600  2100  1300  1300  1900  1650  1200  1750  1900  1800  1500  2000
 [833]  1200  1349  1800  2400  1650  1800  1700  1200  1250  2200  1600  1600  1780  2100  1300  1550
 [849]  2940  1700  2090  1750  1850  1500  1950  1600  1800  1000  1450  3100  1200  1850  2600  1750
 [865]  1700  1600  1250  1800  1900  1300  2400  2200  1500  1700  1700  2000  1000  1600  2000  1199
 [881]  2200  2000  1900  1400  4400  1200  1250  2700  1650  3200  1500  1750  1200  1900  1600  1300
 [897]  1800  1500  1400  2200  1350  2450  1350  1600  2000  1500  1250  2300  1400  1490  2500  1600
 [913]  2500  1300  2100  2700  1990  1500  1100  1600  1600  2400  1150  2000  2200  1250  1100  1350
 [929]  1500  1050  2100  1600  1900  1300  3000  2000  3500  1150  1600  1200  1400  1300  3000  1400
 [945]  2000  2200  1999  1400  1700  1500  1750  1350  1900  1600  1190  1600  2000  1400  1900  1500
 [961]  1950  2050   800  1250  1300  1300  2500  2700  1400  1500  1300  1700  1200  2900  1000  1300
 [977]  1600  1400  1400  1500  1050  2700  2200  1050  4200  1500  1500  1400  1750  1200  2200  1300
 [993]  2700  2800  1500  1800  2200  1200  1800  1800
 [ reached getOption("max.print") -- omitted 15448 entries ]
summary(rent$price)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    250    1350    1600    1763    2000   45000

Wybór kolumn z funkcją subset

subset(rent, select = 1)
subset(rent, select = 1:3)
subset(rent, select = c(1,5, 10))
subset(rent, select = c("id", "price", "flat_area"))
subset(rent, select = id:flat_area)

Wybór wierszy

rent[1,]
rent[1:3,]
rent[c(1, 5, 10), ]
rent[nrow(rent), ] ## tail(rent, 1)

mtcars["Mazda RX4",] # używam tutaj nazw wierszy

Wybieram mieszkania, których cena była niższa lub równa 300 zł

rent[rent$price <= 300, c("id", "price", "ad_title", "flat_area")]
rent[rent$price > 15000, c("id", "price", "ad_title", "flat_area")]
rent[rent$price > 300 & rent$price < 500, 
     c("id", "price", "ad_title", "flat_area")]

Wybieramy wiersze i kolumny z funkcją subset

subset(rent,
       subset = price > 300 & price < 500,
       select = c("id", "price", "ad_title", "flat_area"))

subset(rent,
       price > 300 & price < 500,
       c("id", "price", "ad_title", "flat_area"))

Zadania

subset(rent, flat_for_students == 1 & price < 1000)
subset(rent, flat_for_students == TRUE & price < 1000)
subset(rent, flat_for_students == T & price < 1000)
subset(rent, flat_area <= 50)
subset(rent, flat_area <= 50 & quarter == "centrum") ## to jest źle (mała litera)
subset(rent, flat_area <= 50 & quarter == "Centrum")
subset(rent, (quarter == "Centrum" | quarter == "Rataje") & flat_area <= 50)

subset(rent, quarter %in% c("Centrum", "Rataje") & flat_area <= 50)
subset(rent, flat_deposit == price, c("price", "flat_deposit"))
subset(rent, (flat_deposit/2) > 3*price, c("price", "flat_deposit"))
  1. ceny developera (ataner) -
subset(rent, 
       grepl("ataner", ad_title, ignore.case = T), 
       c("ad_title", "price"))

Tworzymy nowe kolumny

rent$cena_m2

   [1]  46.666667  28.750000  42.592593  32.653061  36.923077  35.294118  31.914894  50.000000  43.333333
  [10]  42.857143  31.746032  41.666667  33.333333  40.000000  27.638889  33.947368  30.000000  36.734694
  [19]  34.210526  23.076923  39.130435  48.387097  44.736842  56.000000  37.672131  31.111111  27.500000
  [28]  33.333333  32.000000  51.612903  42.091837  34.285714  36.111111  41.935484  37.209302  46.046512
  [37]  26.956522  27.586207  35.294118  32.352941  48.148148  44.728435  31.481481  52.857143  54.285714
  [46]  38.000000  38.888889  41.935484  39.062500  33.333333  31.730769  30.000000  35.384615  33.400000
  [55]  39.393939  30.000000  27.500000  28.947368  39.285714  35.416667  43.333333  36.250000  33.802817
  [64]  27.989822  35.526316  53.333333  29.563380  41.269841  41.509434  50.000000  30.534351  29.545455
  [73]  37.500000  32.978723 100.000000  32.323232  42.857143  34.210526  46.774194  24.556962  30.357143
  [82]  39.534884  20.779221  58.139535  30.769231  37.301587  35.714286  28.448276  35.000000  42.592593
  [91]  27.272727  26.086957  39.473684  41.842105  37.500000  30.188679  36.199095  55.555556  29.761905
 [100]  32.000000  43.750000  38.571429  46.875000  49.666667  49.019608  37.333333  44.897959  28.571429
 [109]  43.333333  28.958333  38.000000  45.454545  40.000000  42.105263  24.285714  50.000000  37.878788
 [118]  53.636364  55.000000  40.000000  34.883721  29.166667  37.500000  46.666667  45.312500  44.444444
 [127]  31.111111  36.341463  49.705882  39.473684  39.393939  38.834951  50.000000  26.865672  27.083333
 [136]  36.345455  50.000000  31.967213  46.153846  33.333333  33.962264  36.000000  27.439024  51.851852
 [145]  45.833333  33.333333  35.483871  40.476190  45.833333  46.333333  53.703704  35.264706  33.333333
 [154]  50.000000  50.000000  30.000000  38.235294  41.071429  41.860465  41.666667  30.188679  66.666667
 [163]  34.615385  30.769231  37.549407  45.517241  32.500000  61.111111  34.375000  31.818182  37.500000
 [172]  27.906977  34.615385  38.235294  30.303030  48.888889  29.787234  55.000000  38.333333  38.157895
 [181]  46.000000  58.695652  41.025641  39.062500  42.000000  42.857143  47.619048  30.217391  39.705882
 [190]  45.000000  45.454545  48.571429  40.000000  47.666667  36.792453  30.909091  25.000000  36.666667
 [199]  48.936170  47.962963  28.846154  39.583333  45.945946  46.551724  37.666667  32.051282  31.111111
 [208]  30.000000  31.372549  37.473233  29.000000  28.571429  31.578947  40.312500  44.642857  30.981067
 [217]  49.722222  48.000000  25.000000  31.730769  41.666667  34.146341  37.777778  48.387097  29.000000
 [226]  47.333333  42.857143  30.612245  44.186047  40.476190  25.171625  23.015873  32.758621  32.258065
 [235]  46.428571  39.705882  41.818182  23.333333  33.333333  38.297872  32.653061  31.683168  50.000000
 [244]   8.593750  29.629630  30.612245  29.980000  29.090909  39.603960  35.282258  43.750000  34.210526
 [253]  46.000000  28.000000  36.956522  27.272727  27.000000  37.323944  50.000000  62.756228  39.250000
 [262]  40.625000  42.500000  34.042553  36.734694  34.102564  33.750000  31.775701  39.364303  23.529412
 [271]  42.307692  29.807692  29.815146  44.186047  31.884058  32.075472  31.428571   4.084507  51.666667
 [280]  43.383948  12.307692  34.328358  28.125000  33.333333  34.090909  26.315789  50.000000  36.956522
 [289]  43.750000  28.148148  28.301887  37.363224  43.103448  34.883721  31.818182  36.000000  35.384615
 [298]  43.002346  50.000000  58.823529  27.450980  27.586207  35.701535  32.709091  46.000000  41.304348
 [307]  52.380952  40.540541  43.636364  43.478261  45.095829  60.000000  44.642857  50.000000  35.416667
 [316]  40.593750  33.333333  33.962264  26.666667  27.343750  48.214286  42.592593  35.555556  59.090909
 [325]  40.000000  25.000000  33.333333  27.083333  37.500000  21.575985  27.586207  33.050847  37.500000
 [334]  38.461538  34.042553  33.000000  40.000000  25.806452  57.812500  37.209302  39.622642  39.285714
 [343]  57.142857  58.823529  60.625000  48.611111  41.269841  52.000000  23.529412  36.363636  41.935484
 [352]  47.468354  28.571429  25.000000  29.166667  36.458333  37.500000  44.615385  34.000000  39.428571
 [361]  39.130435  45.000000  42.279412  40.909091  39.556962  30.000000  42.424242  31.250000  38.297872
 [370]  36.363636  42.222222  43.023256  41.176471  41.666667  44.318182  50.000000  25.490196  39.714286
 [379]  33.673469  30.000000  33.333333  39.534884  40.000000  49.180328  30.769231  47.077922  36.111111
 [388]  27.102804  27.358491  30.632411  24.000000  53.333333  38.235294  40.740741  42.857143  45.000000
 [397]  34.042553  28.888889  40.000000  32.692308  37.500000  32.692308  30.833333  45.000000  42.372881
 [406]  25.512821  39.000000  27.500000  32.727273  37.500000  28.125000  24.000000  28.367347  26.666667
 [415]  34.285714  34.821429  25.384615  42.771084  46.153846  39.062500  36.956522  44.186047  49.375000
 [424]  36.363636  42.105263  31.000000  44.000000  39.473684  43.333333  39.534884  34.523810  43.243243
 [433]  50.000000  28.260870  44.000000  35.555556  37.777778  30.612245  40.000000  43.333333  34.000000
 [442]  52.173913  25.714286  42.391304  35.802193  45.000000  42.475000  29.230769  31.914894  39.473684
 [451]  38.742024  39.658328  42.553191  40.000000  25.000000  30.000000  37.931034  28.750000  30.000000
 [460]  26.851852  30.263158  46.875000  41.422594  34.375000  26.507937  35.820896  36.333333  39.583333
 [469]  28.367347  34.285714  40.540541  33.962264  30.357143  18.888889  26.000000  46.439628  33.333333
 [478]  43.343653  40.322581  33.695652  21.357143  35.714286  31.380753  33.333333  27.500000  31.250000
 [487]  37.500000  25.000000  37.735849  17.857143  20.895522  47.368421  32.500000  32.307692  53.846154
 [496]  20.454545  31.481481  37.209302  60.606061  23.582090  42.168675  50.000000  25.000000  32.500000
 [505]  38.709677  26.666667  34.285714  50.000000  43.243243  42.372881  27.083333  67.894737  46.920821
 [514]  46.511628  61.764706  39.200000  54.000000  36.429872  53.164557  28.571429  30.731407  40.740741
 [523]  30.714286  45.714286  62.162162  45.833333  28.846154  32.653061  32.000000  34.610630  31.707317
 [532]  31.481481  40.000000  33.333333  38.000000  42.857143  35.555556  36.111111  34.693878  36.745407
 [541]  33.333333  29.729730  38.000000  43.524136  47.826087  27.000000  34.042553  51.851852  34.210526
 [550]  39.473684  46.808511  33.333333  56.097561  40.000000  30.612245  36.538462  34.042553  51.428571
 [559]  36.206897  25.000000  25.510204  41.666667  28.571429  51.515152  38.888889  32.405405  26.829268
 [568]  28.409091  31.578947  40.579710  39.215686  41.250000  33.928571  37.931034  36.842105  46.666667
 [577]  39.164491  28.166667  97.058824  42.187500  40.540541  31.250000  29.615385  37.755102  30.000000
 [586]  41.666667  26.595745  43.333333  27.450980  34.210526  39.285714  34.108527  33.050847  30.769231
 [595]  31.132075  23.437500  28.000000  73.684211  73.684211  36.792453  34.090909  29.896907  50.000000
 [604]  27.397260  84.210526  33.783784  52.500000  40.833333  82.352941  41.250000  40.061633  35.416667
 [613]  56.060606  54.166667  33.928571  42.452830  27.272727  35.294118  38.338658 110.317460  44.736842
 [622]  42.000000  26.000000  30.952381  31.481481  36.000000  41.666667  37.142857  28.000000  30.666667
 [631]  36.000000  43.280182  37.650602  36.507937  36.486486  53.571429  45.312500  33.653846  31.496063
 [640]  40.752351  28.301887  36.734694  79.500000  41.176471  28.169014  33.333333  44.152745  64.690027
 [649]  40.000000  25.000000  43.750000  42.968750  76.470588  51.724138  36.363636  25.373134  29.000000
 [658]  46.000000  54.545455  55.000000  29.464286  43.000000  37.209302  33.023256  27.500000  43.820225
 [667]  34.375000  39.130435  34.285714  33.333333  43.243243  50.769231  34.534535  45.454545  22.727273
 [676]  31.809145  30.769231  34.313725  38.636364  59.259259  31.645570  42.307692  44.444444  44.416667
 [685]  50.000000  30.888889  36.956522  30.434783  32.653061  40.384615  32.692308  34.209299  37.500000
 [694]  38.461538  51.428571  48.684211  41.463415  47.916667  47.142857  55.000000  46.666667  35.185185
 [703]  28.104575  45.000000  34.375000  31.481481  60.000000  45.205479  37.906137  42.574257  28.048780
 [712]  26.415094  46.333333  40.625000  41.680026  36.666667  29.166667  43.939394  44.444444  30.000000
 [721]  23.809524  63.157895  42.345277  24.461538  37.735849  50.000000  37.037037  28.571429  42.592593
 [730]  25.694444  46.875000  29.473684  31.914894  28.125000  55.000000  39.473684  46.666667  35.714286
 [739]  46.153846  37.894737  36.666667  32.352941  34.883721  29.545455  29.268293  35.294118  28.735632
 [748]  38.000000  36.363636  58.823529  48.484848  34.285714  35.714286  42.857143  37.790698  41.666667
 [757]  27.777778  34.375000  32.352941  62.903226  43.103448  44.318182  84.210526  35.106383  38.333333
 [766]  50.000000  39.024390  31.807496  32.075472  27.777778  27.906977  32.352941  38.461538  40.000000
 [775]  34.000000  40.625000  40.115533  32.222222  41.818182  30.295686  33.333333  32.941176  55.357143
 [784]  37.500000  47.430830  51.724138  42.222222  56.666667  55.000000  38.636364  39.705882  29.032258
 [793]  28.500620  31.666667  41.800000  42.500000  50.000000  27.692308  54.320988  37.209302  47.435897
 [802]  56.000000  33.155543  55.000000  34.920635  43.062201  30.000000  82.608696  32.631579  20.879121
 [811]  27.692308  35.526316  50.000000  43.333333  55.769231  36.956522  50.000000  50.000000  42.000000
 [820]  40.000000  32.653061  58.333333  43.333333  50.000000  38.000000  30.555556  24.489796  51.470588
 [829]  44.906641  32.727273  42.857143  28.078057  22.641509  42.156250  45.000000  40.677966  39.285714
 [838]  38.297872  41.463415  20.000000  52.083333  29.729730  37.209302  40.000000  48.108108  48.837209
 [847]  26.530612  34.444444  40.833333  29.824561  41.800000  56.451613  37.000000  34.090909  49.630949
 [856]  33.826638  34.615385  37.593985  31.521739  57.407407  46.153846  38.541667  43.333333  36.104807
 [865]  34.000000  37.209302  41.666667  47.368421  37.379500  43.333333  42.105263  36.666667  48.231511
 [874]  23.129252  34.000000  30.769231  26.315789  34.042553  20.000000  46.115385  40.740741  38.461538
 [883]  45.238095  28.688525  48.888889  27.906977  35.714286  22.632020  55.000000  51.612903  33.333333
 [892]  43.750000  37.383178  41.304348  32.000000  26.530612  41.570439  31.120332  25.454545  32.835821
 [901]  53.149606  52.127660  32.926829  24.242424  43.478261  31.914894  46.746447  56.097561  45.161290
 [910]  36.341463  34.246575  35.555556  44.642857  28.138528  39.622642  36.000000  52.176193  40.540541
 [919]  34.375000  29.090909  32.000000  43.636364  36.507937  38.461538  37.288136  39.062500  28.947368
 [928]  36.885246  24.590164  45.652174  46.666667  33.333333  44.186047  40.625000  44.776119  44.444444
 [937]  60.344828  32.857143  35.555556  36.363636  45.161290  28.260870  36.144578  42.424242  31.250000
 [946]  37.697053  37.018519  31.111111  38.759690  35.714286  43.750000  34.722222  52.777778  36.363636
 [955]  62.631579  30.188679  45.454545  31.111111  39.583333  20.270270  30.468750  41.836735  48.192771
 [964]  32.894737  41.935484  34.210526  48.076923  49.090909  46.666667  35.714286  37.142857  41.463415
 [973]  27.272727  41.428571  26.315789  37.142857  26.229508  28.000000  48.275862  31.250000  40.384615
 [982]  23.478261  19.515657  37.500000  39.622642  21.428571  31.914894  26.923077  35.000000  36.363636
 [991]  40.000000  36.111111  54.000000  44.800000  68.181818  66.666667  39.285714  40.000000  23.076923
[1000]  37.500000
 [ reached getOption("max.print") -- omitted 15448 entries ]
rent$centrum <- with(rent, quarter == "Centrum")
rent <- transform(rent, rataje = quarter == "Rataje")
head(rent)

Ćwiczenie:

Utworzyć kolumny z funkcją transform

price_log - logartym naturalny ceny area_log - logarytm naturalny powierzchni kawalerka - jeżeli w tytule ogłoszenia jest słowo kawalerka

rent <- transform(rent ,
                  price_log = log(price),
                  area_log = log(flat_area),
                  kawalerka = grepl("kawalerka", ad_title, ignore.case = TRUE))

Tworzymy nową kolumnę z funkcją ifelse

rent$price_tanie_drogie <- 
  with(rent, ifelse(price < 500 | price > 15000, "tanie-drogie", "inne"))

head(rent[, c("price", "price_tanie_drogie")], n=50)

Tworzymy przedziały dla powierzchni z funkcją cut

table(rent$flat_area_cut)


 (0, 10] (10, 20] (20, 30]      30+ 
      84      524     1938    13902

Agregujemy dane

  1. funkcja table
table(rent$flat_rooms)

   1    2    3    4    5    6    7    8    9   10   11 
4438 8283 3118  500   63   21    9    2    5    1    8 
prop.table(table(rent$flat_rooms))*100

           1            2            3            4            5            6            7            8 
26.982003891 50.358706226 18.956712062  3.039883268  0.383025292  0.127675097  0.054717899  0.012159533 
           9           10           11 
 0.030398833  0.006079767  0.048638132 
barplot(table(rent$flat_rooms))

Tablica krzyżowa z funkcją table i xtabs

table(rent$flat_rooms, rent$flat_for_students) 
    
     FALSE TRUE
  1   3154 1284
  2   6190 2093
  3   2303  815
  4    412   88
  5     54    9
  6     18    3
  7      9    0
  8      1    1
  9      3    2
  10     1    0
  11     7    1
tab1 <- with(rent, table(flat_rooms, flat_for_students))

prop.table(tab1)*100
          flat_for_students
flat_rooms        FALSE         TRUE
        1  19.175583658  7.806420233
        2  37.633754864 12.724951362
        3  14.001702335  4.955009728
        4   2.504863813  0.535019455
        5   0.328307393  0.054717899
        6   0.109435798  0.018239300
        7   0.054717899  0.000000000
        8   0.006079767  0.006079767
        9   0.018239300  0.012159533
        10  0.006079767  0.000000000
        11  0.042558366  0.006079767
prop.table(tab1, margin = 2)*100
          flat_for_students
flat_rooms        FALSE         TRUE
        1  25.954575379 29.888268156
        2  50.938117182 48.719739292
        3  18.951612903 18.971135940
        4   3.390388413  2.048417132
        5   0.444371297  0.209497207
        6   0.148123766  0.069832402
        7   0.074061883  0.000000000
        8   0.008229098  0.023277467
        9   0.024687294  0.046554935
        10  0.008229098  0.000000000
        11  0.057603687  0.023277467
xtabs( ~ flat_rooms + flat_for_students, data=rent)
          flat_for_students
flat_rooms FALSE TRUE
        1   3154 1284
        2   6190 2093
        3   2303  815
        4    412   88
        5     54    9
        6     18    3
        7      9    0
        8      1    1
        9      3    2
        10     1    0
        11     7    1

Funkcja summary do podsumowania zbioru lub zmiennej

summary(rent)
      id              date_activ                       date_modif                    
 Length:16448       Min.   :2013-07-19 00:00:00.00   Min.   :2019-05-02 00:00:00.00  
 Class :character   1st Qu.:2019-07-28 00:00:00.00   1st Qu.:2019-09-30 00:00:00.00  
 Mode  :character   Median :2020-01-14 00:00:00.00   Median :2020-03-27 00:00:00.00  
                    Mean   :2019-11-13 09:00:15.75   Mean   :2020-02-29 06:17:09.56  
                    3rd Qu.:2020-06-09 00:00:00.00   3rd Qu.:2020-07-28 00:00:00.00  
                    Max.   :2020-09-25 00:00:00.00   Max.   :2020-09-26 00:00:00.00  
                                                                                     
  date_expire                     individual          price         flat_area         flat_rooms    
 Min.   :2019-06-01 00:00:00.00   Mode :logical   Min.   :  250   Min.   :   4.20   Min.   : 1.000  
 1st Qu.:2019-10-25 00:00:00.00   FALSE:11536     1st Qu.: 1350   1st Qu.:  35.00   1st Qu.: 1.000  
 Median :2020-04-21 00:00:00.00   TRUE :4912      Median : 1600   Median :  45.99   Median : 2.000  
 Mean   :2020-03-24 17:36:05.95                   Mean   : 1763   Mean   :  47.75   Mean   : 2.008  
 3rd Qu.:2020-08-21 00:00:00.00                   3rd Qu.: 2000   3rd Qu.:  55.00   3rd Qu.: 2.000  
 Max.   :2021-04-04 00:00:00.00                   Max.   :45000   Max.   :2200.00   Max.   :11.000  
                                                                                                    
 flat_floor_no   flat_build_year  flat_furnished    flat_rent        flat_deposit   
 Min.   : 0.00   Min.   :     0   Mode :logical   Min.   :    0.0   Min.   :     0  
 1st Qu.: 2.00   1st Qu.:     0   FALSE:8297      1st Qu.:    0.0   1st Qu.:     0  
 Median : 3.00   Median :  1965   TRUE :8151      Median :  300.0   Median :  1400  
 Mean   : 4.18   Mean   :  1175                   Mean   :  286.4   Mean   :  1289  
 3rd Qu.: 5.00   3rd Qu.:  2010                   3rd Qu.:  455.0   3rd Qu.:  2000  
 Max.   :54.00   Max.   :124901                   Max.   :50000.0   Max.   :600067  
 NA's   :673                                                                        
 flat_price_include_rent flat_for_students  flat_heating    flat_status     flat_windows  
 Mode :logical           Mode :logical     Min.   :0.000   Min.   :0.000   Min.   :0.000  
 FALSE:16448             FALSE:12152       1st Qu.:0.000   1st Qu.:0.000   1st Qu.:0.000  
                         TRUE :4296        Median :0.000   Median :0.000   Median :0.000  
                                           Mean   :0.282   Mean   :0.006   Mean   :0.101  
                                           3rd Qu.:0.000   3rd Qu.:0.000   3rd Qu.:0.000  
                                           Max.   :5.000   Max.   :2.000   Max.   :2.000  
                                           NA's   :4148    NA's   :5086    NA's   :4768   
 building_floor_num building_type   building_material   ad_title         ad_seller_id      
 Min.   : 0.00      Min.   :0.000   Min.   :0.000     Length:16448       Length:16448      
 1st Qu.: 3.00      1st Qu.:0.000   1st Qu.:0.000     Class :character   Class :character  
 Median : 4.00      Median :0.000   Median :0.000     Mode  :character   Mode  :character  
 Mean   : 4.29      Mean   :1.518   Mean   :1.182                                          
 3rd Qu.: 5.00      3rd Qu.:4.000   3rd Qu.:0.000                                          
 Max.   :20.00      Max.   :6.000   Max.   :9.000                                          
                    NA's   :2702    NA's   :7530                                           
  ad_promo       flat_balcony    flat_utility_room flat_garage     flat_basement   flat_garden    
 Mode :logical   Mode :logical   Mode :logical     Mode :logical   Mode :logical   Mode :logical  
 FALSE:13814     FALSE:7608      FALSE:15386       FALSE:11794     FALSE:13585     FALSE:15647    
 TRUE :2634      TRUE :8840      TRUE :1062        TRUE :4654      TRUE :2863      TRUE :801      
                                                                                                  
                                                                                                  
                                                                                                  
                                                                                                  
 flat_tarrace    flat_lift       flat_two_level  flat_kitchen_sep flat_air_cond   flat_nonsmokers
 Mode :logical   Mode :logical   Mode :logical   Mode :logical    Mode :logical   Mode :logical  
 FALSE:15129     FALSE:9297      FALSE:16299     FALSE:11918      FALSE:15851     FALSE:14133    
 TRUE :1319      TRUE :7151      TRUE :149       TRUE :4530       TRUE :597       TRUE :2315     
                                                                                                 
                                                                                                 
                                                                                                 
                                                                                                 
 flat_washmachine flat_dishwasher flat_fridge     flat_cooker     flat_oven       flat_tv_device 
 Mode :logical    Mode :logical   Mode :logical   Mode :logical   Mode :logical   Mode :logical  
 FALSE:7982       FALSE:11869     FALSE:7414      FALSE:7774      FALSE:9109      FALSE:13255    
 TRUE :8466       TRUE :4579      TRUE :9034      TRUE :8674      TRUE :7339      TRUE :3193     
                                                                                                 
                                                                                                 
                                                                                                 
                                                                                                 
 flat_internet   flat_television flat_phone      flat_anti_blinds flat_anti_doors_windows flat_intercom  
 Mode :logical   Mode :logical   Mode :logical   Mode :logical    Mode :logical           Mode :logical  
 FALSE:9040      FALSE:9917      FALSE:13948     FALSE:15209      FALSE:12909             FALSE:7045     
 TRUE :7408      TRUE :6531      TRUE :2500      TRUE :1239       TRUE :3539              TRUE :9403     
                                                                                                         
                                                                                                         
                                                                                                         
                                                                                                         
 flat_monitoring flat_alarm_sys  flat_closed_area   quarter             cena_m2        centrum       
 Mode :logical   Mode :logical   Mode :logical    Length:16448       Min.   :  1.00   Mode :logical  
 FALSE:12294     FALSE:16064     FALSE:12377      Class :character   1st Qu.: 31.25   FALSE:14849    
 TRUE :4154      TRUE :384       TRUE :4071       Mode  :character   Median : 37.50   TRUE :1599     
                                                                     Mean   : 39.39                  
                                                                     3rd Qu.: 45.65                  
                                                                     Max.   :750.00                  
                                                                                                     
   rataje          price_log         area_log     kawalerka       price_tanie_drogie  flat_area_cut  
 Mode :logical   Min.   : 5.521   Min.   :1.435   Mode :logical   Length:16448       (0, 10] :   84  
 FALSE:15224     1st Qu.: 7.208   1st Qu.:3.555   FALSE:13563     Class :character   (10, 20]:  524  
 TRUE :1224      Median : 7.378   Median :3.828   TRUE :2885      Mode  :character   (20, 30]: 1938  
                 Mean   : 7.411   Mean   :3.785                                      30+     :13902  
                 3rd Qu.: 7.601   3rd Qu.:4.007                                                      
                 Max.   :10.714   Max.   :7.696                                                      
                                                                                                     
summary(rent$price)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    250    1350    1600    1763    2000   45000

Wyznaczam średnią cenę i powierzchnię

apply(rent[, c("price", "flat_area")], 2, mean)
     price  flat_area 
1762.62117   47.74883 
sapply(rent[, c("price", "flat_area")], mean)
     price  flat_area 
1762.62117   47.74883 
lapply(rent[, c("price", "flat_area")], mean)
$price
[1] 1762.621

$flat_area
[1] 47.74883

Wyznaczam średnią cenę według dzielnicy

aggregate(price ~ quarter, data = rent, FUN = mean)

aggregate(cbind(price, flat_area) ~ quarter, data = rent, FUN = mean)

aggregate(cbind(cena=price, pow=flat_area) ~ quarter, data = rent, FUN = mean)

aggregate(cbind(cena=price, pow=flat_area) ~ quarter + flat_for_students, 
          data = rent, FUN = mean)
NA
NA

Ćwiczenie:

res
---
title: "Zajęcia #2"
author: "Maciej Beręsewicz"
output: 
  html_notebook:
    self-containing: yes
    toc: yes
---

```{r , eval = FALSE}
install.packages("readxl")
```

```{r}
library(readxl)
```


Pytania badawcze:

1. jakie najtańsze a jakie najdroższe
2. jaka dzielnica jest najdroższa
3. gdzie jest najwięcej nieruchomości (dzielnice)
4. rok vs cena
5. ceny developera (ataner) - 
6. mieszkania dla studentów (dostępność, dzielnice)
7. osoby niepalące - 
8. odsetek mieszkań umeblowanych
9. średnia wielkość pokoju
10. cena vs metraż


Wczytujemy dane z pliku csv

- mamy plik na dysku
- mamy link do pliku 

```{r}
rent <- read.csv2(file = "../data-raw/rent-poznan.csv")
head(rent)
```

Wczytamy dane z Excela

```{r}
rent <- read_excel(path = "../data-raw/rent-poznan.xlsx")
rent <- as.data.frame(rent)
head(rent)
```

Wybieramy kolumny ze zbioru rent

```{r}
rent[, 1] # w data.frame wybór jednej kolumny skutkuje wektorem
rent[, 1, drop = FALSE] # drop = FALSE mówi, że mamy dostać ramkę danych
rent[, 1:10]
rent[, ncol(rent), drop = FALSE] # ostatnia kolumna
rent[, c(1, 5, 10)]
rent[, c("id", "price", "flat_area")]
rent$price ## wektor
summary(rent$price)
```

Wybór kolumn z funkcją subset

```{r}
subset(rent, select = 1)
subset(rent, select = 1:3)
subset(rent, select = c(1,5, 10))
subset(rent, select = c("id", "price", "flat_area"))
subset(rent, select = id:flat_area)
```

Wybór wierszy

```{r}
rent[1,]
rent[1:3,]
rent[c(1, 5, 10), ]
rent[nrow(rent), ] ## tail(rent, 1)

mtcars["Mazda RX4",] # używam tutaj nazw wierszy
```

Wybieram mieszkania, których cena była niższa lub równa 300 zł

```{r}
rent[rent$price <= 300, c("id", "price", "ad_title", "flat_area")]
rent[rent$price > 15000, c("id", "price", "ad_title", "flat_area")]
```

```{r}
rent[rent$price > 300 & rent$price < 500, 
     c("id", "price", "ad_title", "flat_area")]
```

Wybieramy wiersze i kolumny z funkcją subset

```{r}
subset(rent,
       subset = price > 300 & price < 500,
       select = c("id", "price", "ad_title", "flat_area"))

subset(rent,
       price > 300 & price < 500,
       c("id", "price", "ad_title", "flat_area"))
```

Zadania

- wybrać mieszkania dla studentów (1 / 0) o cenie mniejszej niż 1000 (133)

```{r}
subset(rent, flat_for_students == 1 & price < 1000)
subset(rent, flat_for_students == TRUE & price < 1000)
subset(rent, flat_for_students == T & price < 1000)
```

- wybrać mieszkania o powierzchni do 50 metrów (10 939)
```{r}
subset(rent, flat_area <= 50)
```

- wybrać mieszkania z Centrum o powierzchni do 50 metrów (1 059)
```{r}
subset(rent, flat_area <= 50 & quarter == "centrum") ## to jest źle (mała litera)
subset(rent, flat_area <= 50 & quarter == "Centrum")
```


- wybrać mieszkania z Centrum lub Rataj o powierzchni do 50 metrów (1 916)

```{r}
subset(rent, (quarter == "Centrum" | quarter == "Rataje") & flat_area <= 50)

subset(rent, quarter %in% c("Centrum", "Rataje") & flat_area <= 50)
```


- wybrać mieszkania, które mają depozyt równy cenie (2 494)

```{r}
subset(rent, flat_deposit == price, c("price", "flat_deposit"))
subset(rent, (flat_deposit/2) > 3*price, c("price", "flat_deposit"))
```

5. ceny developera (ataner) - 

```{r}
subset(rent, 
       grepl("ataner", ad_title, ignore.case = T), 
       c("ad_title", "price"))
```

Tworzymy nowe kolumny

```{r}
rent$cena_m2 <- rent$price/ rent$flat_area
rent[,"cena_m2"] <- rent$price/ rent$flat_area
rent[["cena_m2"]] <- rent$price/ rent$flat_area
```


```{r}
rent$centrum <- with(rent, quarter == "Centrum")
rent <- transform(rent, rataje = quarter == "Rataje")
head(rent)
```

Ćwiczenie:

Utworzyć kolumny z funkcją transform

price_log - logartym naturalny ceny
area_log - logarytm naturalny powierzchni
kawalerka - jeżeli w tytule ogłoszenia jest słowo kawalerka

```{r}
rent <- transform(rent ,
                  price_log = log(price),
                  area_log = log(flat_area),
                  kawalerka = grepl("kawalerka", ad_title, ignore.case = TRUE))
```


Tworzymy nową kolumnę z funkcją ifelse

```{r}
rent$price_tanie_drogie <- 
  with(rent, ifelse(price < 500 | price > 15000, "tanie-drogie", "inne"))

head(rent[, c("price", "price_tanie_drogie")], n=50)
```

Tworzymy przedziały dla powierzchni z funkcją cut

```{r}
rent$flat_area_cut <- cut(rent$flat_area,
                          breaks = c(0, 10, 20, 30, Inf),
                          labels = c("(0, 10]", "(10, 20]", "(20, 30]", "30+"),
                          right = TRUE)

head(rent[, c("flat_area", "flat_area_cut")])

table(rent$flat_area_cut)
```

Agregujemy dane

1. funkcja table

```{r}
table(rent$flat_rooms)
prop.table(table(rent$flat_rooms))*100
barplot(table(rent$flat_rooms))
```
Tablica krzyżowa z funkcją table i xtabs

```{r}
table(rent$flat_rooms, rent$flat_for_students) 

tab1 <- with(rent, table(flat_rooms, flat_for_students))

prop.table(tab1)*100
prop.table(tab1, margin = 2)*100
```
```{r}
xtabs( ~ flat_rooms + flat_for_students, data=rent)
```
Funkcja summary do podsumowania zbioru lub zmiennej

```{r}
summary(rent)
summary(rent$price)
```
Wyznaczam średnią cenę i powierzchnię

```{r}
apply(rent[, c("price", "flat_area")], 2, mean)

sapply(rent[, c("price", "flat_area")], mean)

lapply(rent[, c("price", "flat_area")], mean)
```
Wyznaczam średnią cenę według dzielnicy

```{r}
aggregate(price ~ quarter, data = rent, FUN = mean)

aggregate(cbind(price, flat_area) ~ quarter, data = rent, FUN = mean)

aggregate(cbind(cena=price, pow=flat_area) ~ quarter, data = rent, FUN = mean)

aggregate(cbind(cena=price, pow=flat_area) ~ quarter + flat_for_students, 
          data = rent, FUN = mean)


```

Ćwiczenie:

-  mediana ceny i powierzchni według liczby pokoi wyłącznie dla mieszkań dla studentów (hint: pomoc do funkcji aggregate)

```{r}
res <- aggregate(cbind(cena=price, pow=flat_area) ~ flat_rooms, 
                 data = rent,
                 FUN = median, 
                 subset = flat_for_students == TRUE)

res <- aggregate(cbind(cena=price, pow=flat_area) ~ flat_rooms, 
                 data = subset(rent, flat_for_students == TRUE),
                 FUN = median)
```
```{r}
res
```





